Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah

Islam, S. F. N.; Sholahuddin, Asep; Abdullah, Asnawi

doi:10.1088/1742-6596/1722/1/012016

Cited by 17 publications

(12 citation statements)

References 2 publications

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“…XGB 58 – 61 is an ensemble method that combines weak predictors to generate a strong predictor. XGB prediction for an i instance is where .…”

Section: Methodsmentioning

confidence: 99%

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Martinho,

Hippert,

Goliatt

2023

Sci Rep

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Accurate streamflow prediction is essential for efficient water resources management. Machine learning (ML) models are the tools to meet this need. This paper presents a comparative research study focusing on hybridizing ML models with bioinspired optimization algorithms (BOA) for short-term multistep streamflow forecasting. Specifically, we focus on applying XGB, MARS, ELM, EN, and SVR models and various BOA, including PSO, GA, and DE, for selecting model parameters. The performances of the resulting hybrid models are compared using performance statistics, graphical analysis, and hypothesis testing. The results show that the hybridization of BOA with ML models demonstrates significant potential as a data-driven approach for short-term multistep streamflow forecasting. The PSO algorithm proved superior to the DE and GA algorithms in determining the optimal hyperparameters of ML models for each step of the considered time horizon. When applied with all BOA, the XGB model outperformed the others (SVR, MARS, ELM, and EN), best predicting the different steps ahead. XGB integrated with PSO emerged as the superior model, according to the considered performance measures and the results of the statistical tests. The proposed XGB hybrid model is a superior alternative to the current daily flow forecast, crucial for water resources planning and management.

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“…XGB 58 – 61 is an ensemble method that combines weak predictors to generate a strong predictor. XGB prediction for an i instance is where .…”

Section: Methodsmentioning

confidence: 99%

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Martinho,

Hippert,

Goliatt

2023

Sci Rep

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“…26 In contrast, XGB is a more detailed implementation of GB using accurate estimators to find the best tree models. 27 It uses pre-sorted algorithm algorithms and histogram-based algorithms to calculate the best division. We used the Scikit-Learn machine learning library to develop all machine learning models starting from segmentation, feature extraction, and classification until the model’s performance evaluation.…”

Section: Methodsmentioning

confidence: 99%

Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

et al. 2022

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Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques [Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA)] and seven ML algorithms [k-Nearest Neighbors, Support Vector Machine, Decision Trees, Naïve Bayes, Random Forest (RF), Gradient Boosting, and Extreme Gradient Boosting] were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7 % to 99.8 %, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7 % to 87.6 %. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.

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“…RMSEP is one of the validation tests for the goodness of the method in predicting. The accuracy of the prediction method can be measured by the RMSEP value [16].…”

Section: Rmsep (Root Mean Square Error Prediction)mentioning

confidence: 99%

Function Group Selection of Sembung Leaves (Blumea Balsamifera) Significant to Antioxidants Using Overlapping Group Lasso

Kusnaeni

Soleh

Afendi

et al. 2022

BAREKENG: J. Il. Mat. & Ter.

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Functional groups of sembung leaf metabolites can be detected using FTIR spectrometry by looking at the spectrum's shape from specific peaks that indicate the type of functional group of a compound. There were 35 observations and 1866 explanatory variables (wavelength) in this study. The number of explanatory variables more than the number of observations is high-dimensional data. One method that can be used to analyze high-dimensional data is penalized regression. The overlapping group lasso method is a development of the group-based penalized regression method that can solve the problem of selecting variable groups and members of overlapping groups of variables. The results of selecting the variable groups using the overlapping group lasso method found that the functional groups that were significant for the antioxidants of sembung leaves were C=C Unstructured, CN amide, Polyphenol, Sio2.

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Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah

Cited by 17 publications

References 2 publications

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

Function Group Selection of Sembung Leaves (Blumea Balsamifera) Significant to Antioxidants Using Overlapping Group Lasso

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